{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to use SyntheticControlMethods: an example using the German Reunification study from [Abadie, Diamond and Hainmueller (2015)](https://web.stanford.edu/~jhain/Paper/AJPS2015a.pdf)\n",
    "\n",
    "\n",
    "This notebook is meant provide a worked example of how the package can be used, and how to interpret the different plots. As such, this doubles as both an example and documentation (until I publish standalone docs).\n",
    "\n",
    "In this example, we use data from [Abadie, Diamond and Hainmueller (2015)](https://web.stanford.edu/~jhain/Paper/AJPS2015a.pdf), which estimates the economic impact of the 1990 German reunification on West Germany using the synthetic control method. However, do note that this is not meant to be a replication.\n",
    "\n",
    "The notebook is divided into four sections:\n",
    "1. General data requirements for using Synthetic Controls of any kind\n",
    "1. Using Ordinary Synthetic Controls, Synth()\n",
    "3. Using Penalized Synthetic Controls, Synth(pen=\"auto\")\n",
    "4. Using Differenced Synthetic Controls, DiffSynth(pen=0) and DiffSynth(pen=\"auto\")\n",
    "\n",
    "*Last updated: March 11, 2021.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from SyntheticControlMethods import Synth, DiffSynth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</th>\n",
       "      <th>infrate</th>\n",
       "      <th>trade</th>\n",
       "      <th>schooling</th>\n",
       "      <th>invest60</th>\n",
       "      <th>invest70</th>\n",
       "      <th>invest80</th>\n",
       "      <th>industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>1960</td>\n",
       "      <td>2879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.693181</td>\n",
       "      <td>43.799999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>1961</td>\n",
       "      <td>2929</td>\n",
       "      <td>1.075182</td>\n",
       "      <td>9.444655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>1962</td>\n",
       "      <td>3103</td>\n",
       "      <td>1.116071</td>\n",
       "      <td>9.429324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USA</td>\n",
       "      <td>1963</td>\n",
       "      <td>3227</td>\n",
       "      <td>1.214128</td>\n",
       "      <td>9.470706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USA</td>\n",
       "      <td>1964</td>\n",
       "      <td>3420</td>\n",
       "      <td>1.308615</td>\n",
       "      <td>9.725879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year   gdp   infrate     trade  schooling  invest60  invest70  \\\n",
       "0     USA  1960  2879       NaN  9.693181  43.799999       NaN       NaN   \n",
       "1     USA  1961  2929  1.075182  9.444655        NaN       NaN       NaN   \n",
       "2     USA  1962  3103  1.116071  9.429324        NaN       NaN       NaN   \n",
       "3     USA  1963  3227  1.214128  9.470706        NaN       NaN       NaN   \n",
       "4     USA  1964  3420  1.308615  9.725879        NaN       NaN       NaN   \n",
       "\n",
       "   invest80  industry  \n",
       "0       NaN       NaN  \n",
       "1       NaN       NaN  \n",
       "2       NaN       NaN  \n",
       "3       NaN       NaN  \n",
       "4       NaN       NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import German Reunification data from paper\n",
    "#Can be found in /datasets folder in repo\n",
    "data = pd.read_csv(\"datasets/german_reunification.csv\")\n",
    "data = data.drop(columns=\"code\", axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the dataset used in Abadie, Diamond and Hainmueller (2015). However, the purpose of this notebook is not to provide an exact replication, rather to showcase how to use the package. Consequently, I will not be covering what the variables mean, how they were collected etc. for that, see the original paper (link in title). \n",
    "\n",
    "### Formatting requirements:\n",
    "1. **Numerical variables.** Only the unit identifier, in this case \"country\" can be categorical.\n",
    "2. **The dataset must be sorted by unit and then by time.** That is, all the observations for any unit should appear consecutively in the dataset. In the above example, USA\n",
    "3. **Only one unit identifier, preferably in the form of a string with the unit name.** In the above example, both code and country columns are unit identifiers. To use the package, one must be excluded. This can be done by dropping the column from the dataframe or using the exclude_columns argument when calling Synth() or DiffSynth(). The tables and plots will use the unit identifiers as labels, so it preferable to use descriptive identifiers. FOr example, in this case it is better to use \"country\" instead of \"code\", as \"USA\" is more interpretable than \"1\".\n",
    "4. **The dataset must contain one treated unit and multiple control units.** The package attempts to find a weighted average of the control units that most closely resembles the treated unit, in terms of covariates and outcome, in the pre-treatment period. Of course, finding a good weighted average only works if there are multiple control units.\n",
    "5. **No missing values for the outcome, but okay for covariates**. Synthetic Controls use the entire outcome timeseries, and thus do not accept missing values. As such, you have to deal with missing outcome data yourself before using Synth, e.g. by imputation or dropping units. Because the synthetic control methods works only with the pre-treatment average of the covariates for each unit, it is okay to have missing values for covariates. Indeed, most methods for imputing missing values preserve the mean, and so would not affect the pre-treatment average. As such, missing values are not touched in Synth(). If you would like some kind of missing value imputation, you should do it on the dataframe before you feed it to Synth(). For DiffSynth, things are different. Because the first difference is only defined for consecutive values, it is very sensitive to missing values (more on this under DiffSynth() section below). DiffSynth() automatically uses linear interpolation to impute missing values. If you dislike this, you can impute values yourself before feeding the dataframe to DiffSynth().\n",
    "\n",
    "Note: Variable definitions can be found at the bottom of this notebook, or in Appendix A. of Abadie et al. (2015)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Synth, ordinary Synthetic Controls\n",
    "\n",
    "Synthetic Control is fit using the **Synth()** class which takes the following inputs:\n",
    "\n",
    "### Arguments\n",
    "\n",
    "* **data**: Type: Pandas dataframe. A pandas dataframe containing the dataset. Each row should contain one observation for a unit at a time, including the outcome and covariates. Dataset should be ordered by unit then time.\n",
    "\n",
    "\n",
    "* **outcome_var**: Type: str. Name of outcome column in data, e.g. \"gdp\"\n",
    "\n",
    "\n",
    "* **id_var**: Type: str. Name of unit indicator column in data, e.g. \"country\"\n",
    "\n",
    "\n",
    "* **time_var**: Type: str. Name of time column in data, e.g. \"year\"\n",
    "\n",
    "\n",
    "* **treatment_period**: Type: int. Time of first observation after the treatment took place, i.e. first observation affected by the treatment effect. E.g. 1990 for german reunification.\n",
    "\n",
    "\n",
    "* **treated_unit**: Type: str. Name of the unit that recieved treatment, data[\"id_var\"] == treated_unit.\n",
    "\n",
    "\n",
    "* **n_optim**: Type: int. Default: 10. Number of different initialization values for which the optimization is run. Higher number means longer runtime, but a higher change of a globally optimal solution.\n",
    "\n",
    "\n",
    "* **pen**: Type: float. Default: 0. Penalization coefficient which determines the relative importance of minimizing the sum of the pairwise difference of each individual control unit in the synthetic control and the treated unit, vis-a-vis the difference between the synthetic control and the treated unit. Higher number means pairwise difference matters more. When pen=0, as is the default, the pairwise differences are completely ignored. This means that unless otherwise specificed, Synth() is generating a classic synthetic control, like the ones in Abadie et al. (2015). __If pen=\"auto\"__, the penalization term will be optimized over, along with V and W, using the pre-treatment data. \n",
    "\n",
    "\n",
    "* **exclude_columns**: Type: list. Default: []. List of column names to be excluded from the Synthetic Control. This is practically equivalent to dropping the columns included in the dataframe before running Synth or DiffSynth. That means in the below examples, I could have used exclude_columns=[\"code\"] instead of dropping the column when I loaded the dataset.\n",
    "\n",
    "\n",
    "* **random_seed**: type: int. Default: 0. Random seed is used to create a numpy.random.default_rng(random_seed) object which is subsequently used in all random processes in the code. Random samples are used to initialize covariate importance matrix, V, and \"pen\" (if pen=\"auto\") for optimization.\n",
    "\n",
    "### Methods:\n",
    "\n",
    "Synth objects have 3 methods:\n",
    "* **Synth.plot(...)** contains all the plotting functionalities\n",
    "* **Synth.in_time_placebo(...)** runs in-time placebo tests\n",
    "* **Synth.in_space_placebo(...)** runs in-space placebo tests\n",
    "\n",
    "### Attributes:\n",
    "Synth objects have a 1 attribute:\n",
    "* **original_data**: Original data is an object that stores variables and results Synth(). Important are weight_df and comparison_df, which are tables with summary information (see below). It also contains e.g. variables derived in the data-processing step. You can see all of them by calling Synth.original_data.__ dict__\n",
    "\n",
    "We will be using each of these in the following examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a Synthetic Control and interpreting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit synthetic control\n",
    "sc = Synth(data, \"gdp\", \"country\", \"year\", 1990, \"West Germany\", n_optim=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize\n",
    "sc.plot([\"original\", \"pointwise\", \"cumulative\"], treated_label=\"West Germany\", \n",
    "            synth_label=\"Synthetic West Germany\", treatment_label=\"German Reunification\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot contains three panels. The first panel, \"original\", shows the data and a counterfactual prediction for the post-treatment period. The second panel, \"pointwise\", shows the difference between observed data and counterfactual predictions. This is the pointwise causal effect, as estimated by the model. The third panel, \"cumulative\", adds up the pointwise contributions from the second panel, resulting in a plot of the cumulative effect of the intervention.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0.396718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.603282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Weight\n",
       "USA      0.396718\n",
       "Belgium  0.603282"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get weight matrix of solution\n",
    "sc.original_data.weight_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>pre_rmspe</th>\n",
       "      <th>post_rmspe</th>\n",
       "      <th>post/pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Germany</td>\n",
       "      <td>96.243293</td>\n",
       "      <td>2246.032119</td>\n",
       "      <td>23.337025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unit  pre_rmspe   post_rmspe   post/pre\n",
       "0  West Germany  96.243293  2246.032119  23.337025"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.original_data.rmspe_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weight_df shows the weight assigned to each in the Synthetic Control. In the example above, USA constitutes ~39% of the Synthetic Control. The weights are constrained to be non-negative and to sum to 1.\n",
    "\n",
    "All the units that are assigned 0 weight are excluded from the table, but the complete weight matrix can be retrieved using Synth.original_data.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>pre_rmspe</th>\n",
       "      <th>post_rmspe</th>\n",
       "      <th>post/pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Germany</td>\n",
       "      <td>96.243293</td>\n",
       "      <td>2246.032119</td>\n",
       "      <td>23.337025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unit  pre_rmspe   post_rmspe   post/pre\n",
       "0  West Germany  96.243293  2246.032119  23.337025"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSPE for Synthetic West Germany vs. West Germany\n",
    "#Treated unit is always first unit in rmspe_df\n",
    "sc.original_data.rmspe_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe shows the root mean square prediction error of the outcome of the synthetic control as compared to the original treated unit, in this case West Germany. A low pre_rmspe means that the synthetic control fits the treated unit well in the pre-treatment period, this is important if we are to trust that it provides a good estimate of what the treated units outcome would have been if the treated unit had not recieved the treatment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating covariate balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>West Germany</th>\n",
       "      <th>Synthetic West Germany</th>\n",
       "      <th>WMAPE</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gdp</th>\n",
       "      <td>8169.83</td>\n",
       "      <td>8148.46</td>\n",
       "      <td>1002.35</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infrate</th>\n",
       "      <td>3.39</td>\n",
       "      <td>5.08</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade</th>\n",
       "      <td>45.76</td>\n",
       "      <td>71.27</td>\n",
       "      <td>50.07</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schooling</th>\n",
       "      <td>55.78</td>\n",
       "      <td>37.48</td>\n",
       "      <td>18.31</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest60</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest70</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest80</th>\n",
       "      <td>27.02</td>\n",
       "      <td>22.07</td>\n",
       "      <td>4.94</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <td>39.69</td>\n",
       "      <td>35.30</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           West Germany  Synthetic West Germany    WMAPE  Importance\n",
       "gdp             8169.83                 8148.46  1002.35        0.13\n",
       "infrate            3.39                    5.08     1.69        0.12\n",
       "trade             45.76                   71.27    50.07        0.13\n",
       "schooling         55.78                   37.48    18.31        0.13\n",
       "invest60           0.34                    0.26     0.08        0.12\n",
       "invest70           0.33                    0.27     0.06        0.12\n",
       "invest80          27.02                   22.07     4.94        0.12\n",
       "industry          39.69                   35.30     4.39        0.12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.original_data.comparison_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What the columns show:\n",
    "**self.original_data.treated_unit**: \n",
    "  Unscaled, average covariate values of the treated unit\n",
    "  If method == DSC, then the differenced data is displayed instead\n",
    "\n",
    "**Synthetic Control**: \n",
    "  Unscaled, covariate values of the synthetic control unit\n",
    "  If method == DSC, then the differenced data is displayed instead\n",
    "\n",
    "**WMAPE**:\n",
    "  Weighted Mean Absolute Pairwise Error. For each covariate, how different is each control \n",
    "  unit inside the synthetic control from the treated unit, weighted by the weight assigned to each unit.\n",
    "  This does not change even if method == DSC, as bias scales with value of difference and not change\n",
    "\n",
    "**Importance**:\n",
    "  Leading diagonal of V matrix. How important, relative to other covariates,\n",
    "  is matching on each covariate in the optimization process?\n",
    "  Note that this is computed after rescaling each covariate to be unit variance, \n",
    "  whereas the other columns show the unscaled covariate values.\n",
    "\n",
    "**Control Group Average**:\n",
    "  Simple average of all the units in the control group. Not strictly necessary for anything,\n",
    "  but it is often interesting to see how well the synthetic control is doing. \n",
    "  \n",
    "### How to interpret the table:\n",
    "A good synthetic control will reconstruct (a) the outcome of the treated unit and (b) the covariates of the treated unit over the pre-treatment period. The plots and the RMSPE help us evaluate (a), this table is meant to evaluate (b).\n",
    "If the synthetic control has good fit, the following things should be true:\n",
    "\n",
    "1. Each row of the first two columns should be approximately equal. \n",
    "   This means the synthetic control has reconstructed the treated unit values. In this case, whilst the balance is not perfect, the values are quite similar on all covariates. On it's own, in my opinion, this is not enough to convince us that we have found a strong synthetitic control. However, when considered in combination with the validity tests in the subsequent section, we can be confident we have found a strong synthetic control for West Germany. This is generally true, none of these checks are suffici'ent to show the reliability of a synthetic control, but must be evaluated together–if all point to the conclusion that the synthetic control is good, we can be confident.\n",
    "\n",
    "2. The third column should be small, relative to the values in columns 1 and 2. \n",
    "   The closer to zero, the more similar the individual control units inside the syntetic control are to the treated unit.\n",
    "   The smaller the WMAPE, the lower the potential bias, all else equal. In this case, all of the variables are small, even an order of magnitude smaller, than those in the two first columns–indicating lower risk for bias.\n",
    "\n",
    "3. There is no fixed way to interpret the importance column. Instead, it should be evaluated using domain knowledge.\n",
    "   Is the relative importance assigned to each covariate reasonable given the context?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validity testing of SC\n",
    "\"To evaluate the credibility of our results, we conduct placebo studies where the treatment of interest is reassigned in the data to a year other than 1990 or to countries different from West Germany.\" (Abadie et. al, 2015)\n",
    "\n",
    "### In-time placebo\n",
    "\"We first compare the reunification effect estimated above for West Germany to a placebo effect obtained after reassigning the German reunification in our data to a period before the reunification actually took place. A large placebo estimate would undermine our confidence that the results in Figure 2 are indeed indicative of the economic cost of reunification and not merely driven by lack of predictive power.\" (Abadie et. al, 2015)\n",
    "\n",
    "Specifically, we call in_time_placebo(1982), which reruns the model with 1982 as the first observation after the treatment, about 8 years before the actual reunification in 1990."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oscarengelbrektson/Documents/Minerva/Capstone_Project/SyntheticControlMethods/SyntheticControlMethods/plot.py:256: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.subplot(n_panels, 1, idx)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#In-time placebo\n",
    "#Placebo treatment period is 1982, 8 years earlier\n",
    "sc.in_time_placebo(1982, n_optim=10)\n",
    "\n",
    "#Visualize\n",
    "sc.plot(['in-time placebo'], \n",
    "            treated_label=\"West Germany\",\n",
    "            synth_label=\"Synthetic West Germany\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observing the 'in-time placebo' plot, we see that the outcome of the synthetic control and West Germany do not diverge until the true treatment in 1990, remaining close thereuntil. This increases our confidence in the counterfactual outcome provided by the synthetic control."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### In-space placebo\n",
    "An alternative way to conduct placebo studies is to reassign the treatment in the data to a comparison unit.\n",
    "In this way, we can obtain synthetic control estimates for countries that did not experience the event of interest. Applying this idea to each country in the donor pool allows us to compare the estimated effect of the German reunification on West Germany to the distribution of placebo effects obtained for other countries. We will\n",
    "deem the effect of the German reunification on West Germany significant if the estimated effect for West Germany\n",
    "is unusually large relative to the distribution of placebo effects.\n",
    "\n",
    "To perform this in-space placebo study, we use the method in_space_placebo(). The results are best visualized using the 'rmspe ratio' plot, which shows the distribution of Post-treatment period RMSPE / Pre-treatment period RMSPE for the true treated unit and each of the placebo treated units. The logic is that in the presence of a large treatment effect, the post-treatment difference between the a unit and its synthetic counterpart would be large relative to the pre-treatment difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compute in-space placebos\n",
    "sc.in_space_placebo(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oscarengelbrektson/Documents/Minerva/Capstone_Project/SyntheticControlMethods/SyntheticControlMethods/plot.py:234: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.subplot(n_panels, 1, idx)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize\n",
    "sc.plot(['rmspe ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "West Germany is a clear outlier in the Post-period / pre-period RMSPE distribution, more than twice as extreme as any of the placebo treated units. This increases our confidence in the synthetic control estimates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replication using Penalized Synthetic Control\n",
    "\n",
    "You can think of the ordinary synthetic control method as trying to find weights such that the synthetic control unit is maximally similar to the treated unit AFTER you mix the control units. Penalized Synthetic Controls try to find weights such that the control units are maximally similar AFTER you mix them into a synthetic unit, but also before you mix them. I.e. you would prefer for the units inside the synthetic control to be similar to the treated unit even before you mix them. If the relationship between the outcome and the covariates is non-linear, then the synthetic control is biased and the bias grows with the difference before mixing (more on this in README.md).\n",
    "\n",
    "If you set pen=\"auto\", the synthetic control find a penalization coefficient that strikes a balance between fit before and after mixing, such that the resulting synthetic control is best able to predict the outcome of the treated unit. Beyond that, you interact with the Synth(pen=\"auto\") exactly the same way as you do Synth(pen=0), only the results might look different. In the case of West Germany, they look exactly the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit synthetic control\n",
    "sc_pen = Synth(data, \"gdp\", \"country\", \"year\", 1990, \"West Germany\",\n",
    "               n_optim=30, pen=\"auto\", exclude_columns=[\"code\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: All the code after this point will be exactly the same, only the synthetic control itself will be different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize\n",
    "sc_pen.plot([\"original\", \"pointwise\", \"cumulative\"], treated_label=\"West Germany\", \n",
    "            synth_label=\"Synthetic West Germany\", treatment_label=\"German Reunification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0.396718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.603282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Weight\n",
       "USA      0.396718\n",
       "Belgium  0.603282"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_pen.original_data.weight_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.011276293581664473"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at the penalization coefficient\n",
    "sc_pen.original_data.pen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>pre_rmspe</th>\n",
       "      <th>post_rmspe</th>\n",
       "      <th>post/pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Germany</td>\n",
       "      <td>96.243293</td>\n",
       "      <td>2246.032241</td>\n",
       "      <td>23.337026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unit  pre_rmspe   post_rmspe   post/pre\n",
       "0  West Germany  96.243293  2246.032241  23.337026"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSPE for Synthetic West Germany vs. West Germany\n",
    "#Treated unit is always first unit in rmspe_df\n",
    "sc_pen.original_data.rmspe_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>West Germany</th>\n",
       "      <th>Synthetic West Germany</th>\n",
       "      <th>WMAPE</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gdp</th>\n",
       "      <td>8169.83</td>\n",
       "      <td>8148.46</td>\n",
       "      <td>1002.35</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infrate</th>\n",
       "      <td>3.39</td>\n",
       "      <td>5.08</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade</th>\n",
       "      <td>45.76</td>\n",
       "      <td>71.27</td>\n",
       "      <td>50.07</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schooling</th>\n",
       "      <td>55.78</td>\n",
       "      <td>37.48</td>\n",
       "      <td>18.31</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest60</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest70</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest80</th>\n",
       "      <td>27.02</td>\n",
       "      <td>22.07</td>\n",
       "      <td>4.94</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <td>39.69</td>\n",
       "      <td>35.30</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           West Germany  Synthetic West Germany    WMAPE  Importance\n",
       "gdp             8169.83                 8148.46  1002.35        0.13\n",
       "infrate            3.39                    5.08     1.69        0.12\n",
       "trade             45.76                   71.27    50.07        0.13\n",
       "schooling         55.78                   37.48    18.31        0.13\n",
       "invest60           0.34                    0.26     0.08        0.12\n",
       "invest70           0.33                    0.27     0.06        0.12\n",
       "invest80          27.02                   22.07     4.94        0.12\n",
       "industry          39.69                   35.30     4.39        0.12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_pen.original_data.comparison_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oscarengelbrektson/Documents/Minerva/Capstone_Project/SyntheticControlMethods/SyntheticControlMethods/plot.py:256: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.subplot(n_panels, 1, idx)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#In-time placebo\n",
    "#Placebo treatment period is 1982, 8 years earlier\n",
    "sc_pen.in_time_placebo(1982, n_optim=10)\n",
    "\n",
    "#Visualize\n",
    "sc_pen.plot(['in-time placebo'], \n",
    "            treated_label=\"West Germany\",\n",
    "            synth_label=\"Synthetic West Germany\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oscarengelbrektson/Documents/Minerva/Capstone_Project/SyntheticControlMethods/SyntheticControlMethods/plot.py:234: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.subplot(n_panels, 1, idx)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compute in-space placebos\n",
    "sc_pen.in_space_placebo(15)\n",
    "\n",
    "#Visualize\n",
    "sc_pen.plot(['rmspe ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Using Differenced Synthetic Control, DiffSynth\n",
    "\n",
    "### What is a Differenced Synthetic Control?\n",
    "\n",
    "\n",
    "A key insight that Consequently, DiffSynth has a larger solution space.\n",
    "\n",
    "In general, I recommend using DiffSynth if the Synth does not do a good job–there is a chance DiffSynth might because of the extra flexibility. For example, if the outcome of the treated unit is more extreme than those of any or most of the control units, then ordinary synthetic control is bound to fail, as the convexity constraint on the weights (can never sum to more than 1), means the best synthetic control will assign all weight to the most extreme control unit. In this case, DiffSynth will not have a problem as it tries only to construct a synthetic control that changes the same way as the treated unit, not one that changes the same way and has the same level as the treated unit. Further, __Use DiffSynth with pen=\"auto\"__ to mitigate bias that could follow from increased flexibility.\n",
    "\n",
    "Note: Differenced Synthetitic Controls do not exist in the literature - I propose them in my forthcoming thesis advised by Professor Alexis Diamond, one of the coauthors on the works introducing the original Synthetic Control (Abadie, Diamond & Hainmueller, 2010; Abadie, Diamond & Hainmueller, 2015).\n",
    "\n",
    "\n",
    "### How to use Differenced Synthetic Controls\n",
    "Differerenced synthetic controls are called using DiffSynth() instead of Synth(). I takes one additional, optional argument, apart from that you use it exactly the same way as Synth and interpret the results the same way. There is one exception, in terms, of interpretation: the comparison table, it will show the average _change_ instead of the average values of the covariates (example below).\n",
    "\n",
    "Differenced Synthetic Control is fit using the DiffSynth() method, which takes the following inputs:\n",
    "\n",
    "* **not_diff_cols**: Type: list. Default: []. List of column names to omit from pre-processing, e.g. compute the first difference for. Typically, columns should be included if the proportion of missing values is high. This is because the first difference is only defined for two consecutive values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsc = DiffSynth(data, \"gdp\", \"country\", \"year\", 1990, \"West Germany\", \n",
    "                  not_diff_cols=[\"schooling\", \"invest60\", \"invest70\", \"invest80\"],\n",
    "                  n_optim=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because \"schooling\", \"invest60\", \"invest70\" and \"invest80\" all have 97% missing data, i.e. one observation per variable per unit, taking the first difference of them is not fruitful. If we do it before imputing missing values, the change is not defined as there is nothing to compare it to. If we do it after imputing values, regardless of which imputation strategy we use, for any unit, we would assign the same value to each time period. In turn, this would mean that the change between any two time periods would be zero. Either way, the informational value of the covariates would be lost.\n",
    "\n",
    "This is a general weakness of the first difference approach–in these cases it is best to not compute the first difference, by including the columns in the not_diff_cols argument. That said, there are still benefits to be reaped by differencing the outcome and the remaining covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize\n",
    "dsc.plot([\"original\", \"pointwise\", \"cumulative\"], treated_label=\"West Germany\", \n",
    "            synth_label=\"Synthetic West Germany\", treatment_label=\"German Reunification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0.396783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.524202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>0.027761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>0.051254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Weight\n",
       "USA          0.396783\n",
       "Belgium      0.524202\n",
       "Portugal     0.027761\n",
       "New Zealand  0.051254"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsc.original_data.weight_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134.4429712465385"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The differenced synthetic control outcome has been shifted upwards by 134 units\n",
    "dsc.original_data.synth_constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>pre_rmspe</th>\n",
       "      <th>post_rmspe</th>\n",
       "      <th>post/pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Germany</td>\n",
       "      <td>120.346826</td>\n",
       "      <td>1927.299384</td>\n",
       "      <td>16.014543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unit   pre_rmspe   post_rmspe   post/pre\n",
       "0  West Germany  120.346826  1927.299384  16.014543"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RMSPE for Synthetic West Germany vs. West Germany\n",
    "dsc.original_data.rmspe_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It should be noted that the pre-treatment fit on the outcome, whilst still excellent, is somewhat worse than that or ordinary Synth for this application. Notice, however, that in the outcome plot DiffSynth matches the outcome perfectly over the last time-periods before the German Reunification–Synth, both penalized and ordinary, overestimated West Germany's GDP. This results in a somewhat lower treatment effect estimate as indicated by post_rmspe––which may actually be more appropriate/accurate than Synth and PenSynth––and therefore also a lower post/pre ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>West Germany</th>\n",
       "      <th>Synthetic West Germany</th>\n",
       "      <th>WMAPE</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gdp</th>\n",
       "      <td>576.21</td>\n",
       "      <td>572.96</td>\n",
       "      <td>1115.58</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infrate</th>\n",
       "      <td>3.39</td>\n",
       "      <td>5.52</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade</th>\n",
       "      <td>46.11</td>\n",
       "      <td>67.65</td>\n",
       "      <td>45.80</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schooling</th>\n",
       "      <td>55.27</td>\n",
       "      <td>38.28</td>\n",
       "      <td>17.73</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest60</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest70</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invest80</th>\n",
       "      <td>27.02</td>\n",
       "      <td>22.39</td>\n",
       "      <td>4.63</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <td>40.10</td>\n",
       "      <td>36.27</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           West Germany  Synthetic West Germany    WMAPE  Importance\n",
       "gdp              576.21                  572.96  1115.58        0.12\n",
       "infrate            3.39                    5.52     2.13        0.15\n",
       "trade             46.11                   67.65    45.80        0.16\n",
       "schooling         55.27                   38.28    17.73        0.15\n",
       "invest60           0.34                    0.26     0.08        0.08\n",
       "invest70           0.33                    0.27     0.06        0.10\n",
       "invest80          27.02                   22.39     4.63        0.07\n",
       "industry          40.10                   36.27     4.70        0.16"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsc.original_data.comparison_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference in interpreting DiffSynth results from Synth are in the first two columns of this table. Notice the West Germany values are different for every covariate (except the ones include in not_diff_cols). This is because they show the average change in the covariate value from the previous year (previous time period, in general/any application), instead of the average value. The differenced synthetic control tries to construct a synthetic control that changes the same way as the treated unit, so that the outcomes are parallel in the pre-treatment period. Similar to Difference-in-Differences methods, a constant offset can then be included to shift the synthetic control pre-treatment outcome up to the same level as the treated unit.\n",
    "\n",
    "__WMAPE__: Notice that the third column is still displaying unitwise average differences in the values of the covariates, _not_ the change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validity testing of DSC\n",
    "The validity tests for DSC work and can be interpreted the same way as they are for SC.\n",
    "\n",
    "### In-time placebo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oscarengelbrektson/Documents/Minerva/Capstone_Project/SyntheticControlMethods/SyntheticControlMethods/plot.py:256: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.subplot(n_panels, 1, idx)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#In-time placebo\n",
    "#Placebo treatment period is 1982, 8 years earlier\n",
    "dsc.in_time_placebo(1982)\n",
    "\n",
    "#Visualize\n",
    "dsc.plot(['in-time placebo'], \n",
    "            treated_label=\"West Germany\",\n",
    "            synth_label=\"Synthetic West Germany\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the striking similarity of the in-time placebo synthetic control unit and the real synthetic control unit generated by DiffSynth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In-space placebo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/oscarengelbrektson/Documents/Minerva/Capstone_Project/SyntheticControlMethods/SyntheticControlMethods/plot.py:234: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = plt.subplot(n_panels, 1, idx)\n"
     ]
    },
    {
     "data": {
      "image/png": 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HZBIQvGQ47muH4yTJDaZ2eW13XzP1+p+7u6vqoiSf7u6Lhn4vyWQqwwVJfqaqTsrkfdiQ5Kih5iR5w/B7S5KfHJZfnuSfkjwvyS8kecUytZ+U5KRdOV8AAADYF6wkKFh4TsH3ZzL14BNJfivJlzO5UK4kl3T3vZfY9yeS3D/JQ5P8blV9/06OdUmSu2Vyp/5zSY6uqqcmOTST0Q9fHJ5bsJQrF72+evh97dTywut1VXW7TEYl3KO7vzBMSTh4if2vyfA+dfcnqurTVfXAJPfMN0cXfIvuPi2TcCNV1Ts8YwAAANiHrOTrEd+T5CFJPt/d13T355Mclsmw/vck+UiSW1TVvZOkqg6qqjsPDz+8TXf/RybPHrhJJhf8X0ly42WO9aeZBAp3mmo7JEm6+8tJPlZVPz0cp6rqrrt0tt/qOzIJF75UVbdK8mMr3O9lmUxBWDyCAQAAANa8lQQFF2XybQfvW9T2pe6+vLu/nuT4JM+qqgszGdJ/TCZTEP52GPp/fpK/7O4vJvnnJI9c6mGGw/SAX0/yyqr6SFWdk8kDE/9u2OSEJE8YjnNJkofvxjkvHOvCoa4PD/2fs8Jdz8wk8Fhy2gEAAACsZdVtZPyuqKrNmTyMcfE3Niy3fR974smrXBUAAACzctYZp2atXVtX1Zbh4f7fZiXPKGBQVU9P8itZ5tkEAAAAsNatZOoBg+5+ZnfftrvfPetaAAAAYDUICgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAUXX3rGvYr61fv77n5+dnXQYAAACrZMMRG7P9sm2zLmOXVNWW7t681Lp1e7uYA82mTZsyNzc36zIAAABgRUw9AAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYOTrEVfZ1q1bU1WzLgNgptbidwsDAByoBAWrbH5+PseeePKsywCYqbPOOHXWJQAAsEKmHgAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAAKM1HRRU1a2q6u+q6qNVtaWq3ltVj5x1XQAAALBWrdmgoKoqyRuTvKu7v7u7757kUUluvWi7dTMoDwAAANakNRsUJHlgkq9390sWGrr74939/Kp6fFWdWVXvSPL2qrpRVb28qt5fVedX1cOTpKquX1XPrqoPVNXWqvrlhb6q6mlVdVFVXVhVzxzavqeq3jyMXji7qu64t08aAAAAVtNavtt+5yTn7WD93ZJs6u7PV9UfJ3lHd/9CVR2W5P1V9e9JTkjype6+R1XdIMk5VfXWJHdM8vAkP9jdX62qmw19npbkid39X1X1g0lelElgAQAAAPuFtRwUfIuqemGS+yb5epIXJnlbd39+WP0jSR5WVU8dXh+c5LuG9k1VdfzQfpMkt0/yoCSv6O6vJskQNhya5Jgkr53MekiS3GCZWk5KctIePD0AAADYK9ZyUHBJkp9aeNHdv1pVhyeZG5qunNq2kvxUd39kuoPhOQdP6u63LGr/0SWOd70kX+zuo3dWWHeflsnog1RV7/xUAAAAYN+wlp9R8I4kB1fVr0y1HbLMtm9J8qQhGEhV/cBU+69U1UFD+/dV1Y2SvC3Jz1fVIUP7zbr7y0k+VlU/PbRVVd11j58VAAAAzNCaDQq6u5M8IsmxVfWxqnp/kr9J8rQlNj81yUFJtlbVJcPrJHlZkg8mOa+qLk7yV0nWdfebk5yZZK6qLkiyMGXhhCRPqKoLMxnR8PDVODcAAACYlZpcb7NaqqqPPfHkWZcBMFNnnXFq/PcGAGDfUVVbunvzUuvW7IgCAAAAYM8TFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMKrunnUN+7X169f3/Pz8rMsAmKkNR2zM9su2zboMAAAGVbWluzcvtW7d3i7mQLNp06bMzc3NugwAAABYEVMPAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGPl6xFW2devWVNWsywDWoA1HbMz2y7bNugwAAA4wgoJVNj8/n2NPPHnWZQBr0FlnnDrrEgAAOACZegAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIz2uaCgqq6pqguq6pKqurCqfquq9pk6q+qKWdcAAAAAq2XdrAtYwlXdfXSSVNUtk/xdku9I8gezLKqqKknNsgYAAABYbfvMnfqldPdnkpyU5Ndq4vpV9eyq+kBVba2qX06Sqjquqt5ZVa+rqg9X1auGC/tU1aVV9SfDKIW5qrpbVb2lqv6nqp44bHNoVb29qs6rqouq6uFD+5FV9ZGqemWSi5PcZqG2qjq8qt5bVT+xt98XAAAAWC374oiCb9HdH62q6ye5ZZKHJ/lSd9+jqm6Q5Jyqeuuw6Q8kuXOS7UnOSXKfJO8e1v1vdx9dVc9Ncvqw7uBMLv5fkuRrSR7Z3V+uqsOTvK+qzhz2vX2Sx3X3+5KkqlJVt0pyZpLf6+63reb5AwAAwN60zwcFi/xIkk1Vdfzw+iaZXMh/Pcn7u3tbklTVBUmOzDeDgoWL/ouSHNrdX0nylaq6uqoOS3Jlkj+uqvsnuTbJxiS3Gvb5+EJIMDgoyduT/Gp3n7VUkVV1UiYjIQAAAGBN2eeDgqr67iTXJPlMJs8IeFJ3v2XRNscluXqq6Zp867ktrLt20XbXDtudkOQWSe7e3fNVdWkmIw6SSYgw7RtJtiT50SRLBgXdfVqS04baeienCAAAAPuMffoZBVV1i0ymBryguzvJW5L8SlUdNKz/vqq60R441E2SfGYICR6Q5LY72LaT/EKSO1bV0/bAsQEAAGCfsS+OKLjhMHXgoEzu3p+R5M+HdS/LZErBecPDCj+b5BF74JivSvLPVXVRkrkkH97Rxt19TVU9OsmZVfWV7n7RHqgBAAAAZq4mN+pZLVXVx5548qzLANags844Nf4dDQDAaqiqLd29eal1+/TUAwAAAGDvEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADCq7p51Dfu19evX9/z8/KzLANagDUdszPbLts26DAAA9kNVtaW7Ny+1bt3eLuZAs2nTpszNzc26DAAAAFgRUw8AAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAY+XrEVbZ169ZU1azLgAPahiM2Zvtl22ZdBgAArAmCglU2Pz+fY088edZlwAHtrDNOnXUJAACwZph6AAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjPbboKCqfreqLqmqrVV1QVX94G708bCqevpq1AcAAAD7onWzLmA1VNW9kzwkyd26++qqOjzJ+l3tp7vPTHLmnq4PAAAA9lX764iCDUku7+6rk6S7L+/u7VV1aVX9aVVdVFXvr6rvTZKqemhVnVtV51fVv1fVrYb2x1fVC4bl06vqL6vqPVX10ao6fmZnBwAAAKtkfw0K3prkNlX1n1X1oqo6dmrdl7r7+5O8IMnzhrZ3J7lXd/9Aklcn+Z1l+t2Q5L6ZjFZ45nIHr6qTqmququau43kAAADAXrVfTj3o7iuq6u5J7pfkAUleM/Wsgb+f+v3cYfnWwzYbMpmi8LFlun5jd1+b5IMLow6WOf5pSU5Lkqrq63QyAAAAsBftryMK0t3XdPc7u/sPkvxakp9aWDW92fD7+UleMIw0+OUkBy/T7dVTy7Un6wUAAIB9wX4ZFFTVHarq9lNNRyf5+LD8s1O/3zss3yTJZcPy41a9QAAAANhH7ZdTD5IcmuT5VXVYkm8k+e8kJ2XybIGbVtXWTEYHPHrY/pQkr62qLyR5R5Lb7e2CAQAAYF9Q3QfOFPqqujTJ5u6+fC8es4898eS9dThgCWedcWoOpH/XAQDAzlTVlu7evNS6/XLqAQAAALB79tepB0vq7iNnXQMAAADsy4woAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABG1d2zrmG/tn79+p6fn591GXBA23DExmy/bNusywAAgH1GVW3p7s1LrVu3t4s50GzatClzc3OzLgMAAABWxNQDAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARr4ecZVt3bo1VTXrMuCAseGIjdl+2bZZlwEAAGuWoGCVzc/P59gTT551GXDAOOuMU2ddAgAArGmmHgAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAAKM1FxRUVVfVc6ZeP7WqTplhSQAAALDfWHNBQZKrk/xkVR2+OztX1bo9XA8AAADsN9ZiUPCNJKclecriFVV1ZFW9o6q2VtXbq+q7hvbTq+olVXVukj+tqouq6rCa+FxVPXbY7pVV9cNDP2dX1XnDzzFT6x8xdbxXVdXD98ZJAwAAwN6wFoOCJHlhkhOq6iaL2p+f5G+6e1OSVyX5y6l1t05yTHf/ZpJzktwnyZ2TfDTJ/YZt7p3kPUk+k+SHu/tuSX52qp+/TvL4JBmOfUySf1lcXFWdVFVzVTV3Hc8TAAAA9qo1GRR095eTvDLJkxetuneSvxuWz0hy36l1r+3ua4bls5Pcf/h5cZLvr6qNSb7Q3VcmOSjJS6vqoiSvTXLUcNyzkty+qm6R5NFJXt/d31iivtO6e3N3b77uZwsAAAB7z5oMCgbPS/KEJDda4fZXTi2/K5NRBPdL8s4kn01yfCYBQjKZ1vDpJHdNsjnJ+ql9X5nkMUl+PsnLd6tyAAAA2Eet2aCguz+f5B8yCQsWvCfJo4blE/LNC//F+34iyeFJbt/dH03y7iRPzSRASJKbJPlkd1+b5MQk15/a/fQkvzH088E9cCoAAACwz1izQcHgOZlc8C94UpKfr6qtmVzg//oO9j03yX8Oy2cn2ZhJYJAkL0ryuKq6MMkdMzUaobs/neRDSV6xJ04AAAAA9iVr7qsCu/vQqeVPJzlk6vXHkzxwiX0ev0TbiVPL78lUaNLd/5Vk09TmT1tYqKpDktw+yd/v7jkAAADAvmqtjyjYq6rqQZmMJnh+d39p1vUAAADAnrbmRhTMUnf/e5LbzroOAAAAWC1GFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAo+ruWdewX1u/fn3Pz8/Pugw4YGw4YmO2X7Zt1mUAAMA+raq2dPfmpdat29vFHGg2bdqUubm5WZcBAAAAK2LqAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNfj7jKtm7dmqqadRlwQNhwxMZsv2zbrMsAAIA1TVCwyubn53PsiSfPugw4IJx1xqmzLgEAANY8Uw8AAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAIDRulkXcF1V1TVJLppqekR3XzqjcgAAAGBNW/NBQZKruvvopVZUVSWp7r5275YEAAAAa9N+N/Wgqo6sqo9U1SuTXJzkNlX14qqaq6pLquoZU9teWlXPqKrzquqiqrrj0H5oVb1iaNtaVT81tP9IVb132P61VXXobM4SAAAAVsf+EBTcsKouGH7+cWi7fZIXdfedu/vjSX63uzcn2ZTk2KraNLX/5d19tyQvTvLUoe3kJF/q7u/v7k1J3lFVhyf5vSQPGrafS/KbSxVUVScNwcTcHj9bAAAAWEX73dSDqjoyyce7+31T2/xMVZ2UyfluSHJUkq3DujcMv7ck+clh+UFJHrWwc3d/oaoeMux3zmRGQ9Ynee9SBXX3aUlOG+rp63BuAAAAsFftD0HBUq5cWKiq22UyUuAewwX/6UkOntr26uH3Ndnx+1FJ3tbdj97DtQIAAMA+Y3+YerAz35FJcPClqrpVkh9bwT5vS/KrCy+q6qZJ3pfkPlX1vUPbjarq+1ahXgAAAJiZ/T4o6O4Lk5yf5MNJ/i7JOSvY7Q+T3LSqLq6qC5M8oLs/m+TxSf6+qrZmMu3gjqtTNQAAAMzGmp960N2HLnp9aZK7LGp7/DL7Hjm1PJfkuGH5iiSPW2L7dyS5x3WrGAAAAPZd+/2IAgAAAGDlBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIyqu2ddw35t/fr1PT8/P+sy4ICw4YiN2X7ZtlmXAQAA+7yq2tLdm5dat25vF3Og2bRpU+bm5mZdBgAAAKyIqQcAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMfD3iKtu6dWuqatZlwJqy4YiN2X7ZtlmXAQAAByRBwSqbn5/PsSeePOsyYE0564xTZ10CAAAcsEw9AAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARvtsUFBVj6iqrqo7Xof9j9qN/R5fVS8Ylp9YVY/dneMDAADAWrTPBgVJHp3k3cPv3fGIJEsGBVW1biUddPdLuvuVu3l8AAAAWHP2yaCgqg5Nct8kT0jyqKHtuKp609Q2L6iqxw/Lz6yqD1bV1qr6s6o6JsnDkjy7qi6oqu+pqndW1fOqai7Jr1fVQ6vq3Ko6v6r+vaputUQdp1TVU4flX6qqD1TVhVX1+qo6ZNXfCAAAANjLVnRnfQYenuTN3f2fVfW5qrr7chtW1c2TPDLJHbu7q+qw7v5iVZ2Z5E3d/bphuyRZ392bh9c3TXKvYZ9fTPI7SX5rBzW9obtfOuz7h5mEGM9fpqaTkpy0i+cMAAAAM7dPjijIZLrBq4flV2fH0w++lORrSf66qn4yyVd3sO1rppZvneQtVXVRkt9Ocued1HSXqjp72P6EHW3f3ad19+aFUAIAAADWin0uKKiqmyV5YJKXVdWlmVzE/0ySa/Kt9R6cJN39jST3TPK6JA9J8uYddH/l1PLzk7ygu78/yS8v9LcDpyf5tWH7Z6xgewAAAFhz9rmgIMnxSc7o7tt295HdfZskH8uk1qOq6gZVdViSH0rG5xncpLv/NclTktx16OcrSW68g+PcJMllw/LjVlDXjZN8sqoOymREAQAAAOx39sWg4NFJ/nFR2+szeajhPyS5ePh9/rDuxkneVFVbM/mWhN8c2l+d5LeHhxV+zxLHOSXJa6tqS5LLV1DXyUnOTXJOkg+v+GwAAABgDanunnUN+7Wq6mNPPHnWZcCactYZp8a/mwAAYPVU1Zblnqu3L44oAAAAAGZEUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwKi6e9Y17NfWr1/f8/Pzsy4D1pQNR2zM9su2zboMAADYb1XVlu7evNS6dXu7mAPNpk2bMjc3N+syAAAAYEVMPQAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGDk6xFX2datW1NVsy4D1owNR2zM9su2zboMAAA4YAkKVtn8/HyOPfHkWZcBa8ZZZ5w66xIAAOCAZuoBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAw2m+Dgqq6YtY1AAAAwFqz3wYFAAAAwK7br4OCqjq0qt5eVedV1UVV9fCh/ciq+nBVvaqqPlRVr6uqQ4Z1v19VH6iqi6vqtKqqof2dVfWsqnp/Vf1nVd1vlucGAAAAq2G/DgqSfC3JI7v7bkkekOQ5Cxf+Se6Q5EXdfackX07y/w3tL+jue3T3XZLcMMlDpvpb1933TPIbSf5guYNW1UlVNVdVc3v2dAAAAGB17e9BQSX546ramuTfk2xMcqth3Se6+5xh+W+T3HdYfkBVnVtVFyV5YJI7T/X3huH3liRHLnfQ7j6tuzd39+Y9cxoAAACwd6ybdQGr7IQkt0hy9+6er6pLkxw8rOtF23ZVHZzkRUk2d/cnquqUqe2T5Orh9zXZ/987AAAADkD7+4iCmyT5zBASPCDJbafWfVdV3XtY/rkk7843Q4HLq+rQJMfvvVIBAABg9vbLoKCq1mVy9/9VSTYP0wgem+TDU5t9JMmvVtWHktw0yYu7+4tJXprk4iRvSfKBvVk3AAAAzNr+Onz+zkn+p7svT3LvxSur6sgk3+juxyxe192/l+T3lmg/bmr58uzgGQUAAACwVu13Iwqq6olJ/j5LXOwDAAAAO7bfjSjo7pckeclOtrk0yV32SkEAAACwhux3IwoAAACA3ScoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGBU3T3rGvZr69ev7/n5+VmXAWvGhiM2Zvtl22ZdBgAA7Neqakt3b15q3bq9XcyBZtOmTZmbm5t1GQAAALAiph4AAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAw8vWIq2zr1q2pqlmXAXvNhiM2Zvtl22ZdBgAAsJsEBatsfn4+x5548qzLgL3mrDNOnXUJAADAdWDqAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMFoTQUFVXVNVF1TVhVV1XlUds4J9rljBNi+rqqP2TJUAAACw9q2bdQErdFV3H50kVfWjSf4kybHXtdPu/sXr2gcAAADsT9bEiIJFviPJFxZeVNVvV9UHqmprVT1j8cZVdb2qelFVfbiq3lZV/1pVxw/r3llVm4flK6b2Ob6qTh+WT6+qF1fV+6rqo1V1XFW9vKo+tLANAAAA7C/WyoiCG1bVBUkOTrIhyQOTpKp+JMntk9wzSSU5s6ru393vmtr3J5McmeSoJLdM8qEkL9/F4980yb2TPCzJmUnuk+QXk3ygqo7u7gumN66qk5KctIvHAAAAgJlbKyMKruruo7v7jkkenOSVVVVJfmT4OT/JeUnumElwMO2+SV7b3dd296eS/MduHP+fu7uTXJTk0919UXdfm+SSTEKIb9Hdp3X35u7evBvHAgAAgJlZKyMKRt393qo6PMktMhlF8Cfd/Vd7ouup5YMXrbt6+H3t1PLC6zX3HgIAAMBy1sqIglFV3THJ9ZN8LslbkvxCVR06rNtYVbdctMs5SX5qeFbBrZIct0zXn66qO1XV9ZI8cnWqBwAAgH3bWrkbvvCMgmQyiuBx3X1NkrdW1Z2SvHcyEyFXJHlMks9M7fv6JD+U5INJPpHJFIUvLXGMpyd5U5LPJplLcuiePw0AAADYt9Vk6v3+raoO7e4rqurmSd6f5D7D8wr2xrH72BNP3huHgn3CWWecmgPh3ysAALCWVdWW5Z6rt1ZGFFxXb6qqw5KsT3Lq3goJAAAAYK05IIKC7j5u1jUAAADAWrDmHmYIAAAArB5BAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAACj6u5Z17BfW79+fc/Pz8+6DNhrNhyxMdsv2zbrMgAAgB2oqi3dvXmpdev2djEHmk2bNmVubm7WZQAAAMCKmHoAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADAyNcjrrKtW7emqmZdBuxxG47YmO2XbZt1GQAAwB4mKFhl8/PzOfbEk2ddBuxxZ51x6qxLAAAAVoGpBwAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwOiACwqq6orh95FV9XMr2P7Iqrp49SsDAACA2TvggoIpRybZaVAAAAAAB5IDOSh4ZpL7VdUFVfWUYeTA2VV13vBzzOIdqupdVXX01Ot3V9Vd92bRAAAAsJoO5KDg6UnO7u6ju/u5ST6T5Ie7+25JfjbJXy6xz18neXySVNX3JTm4uy9cvFFVnVRVc1U1t2rVAwAAwCo4kIOCxQ5K8tKquijJa5MctcQ2r03ykKo6KMkvJDl9qY66+7Tu3tzdm1erWAAAAFgN62ZdwD7kKUk+neSumQQoX1u8QXd/tareluThSX4myd33aoUAAACwyg7koOArSW489fomSbZ197VV9bgk119mv5cl+edMpi18YZVrBAAAgL3qQJ56sDXJNVV1YVU9JcmLkjyuqi5McsckVy61U3dvSfLlJK/Ya5UCAADAXnLAjSjo7kOH3/NJHrho9aap5acN212a5C4LjVV1RCYBy1tXtVAAAACYgQN5RMEuq6rHJjk3ye9297WzrgcAAAD2tANuRMF10d2vTPLKWdcBAAAAq8WIAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgVN096xr2a+vXr+/5+flZlwF73IYjNmb7ZdtmXQYAALAbqmpLd29eat26vV3MgWbTpk2Zm5ubdRkAAACwIqYeAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMPL1iKts69atqapZlwF71IYjNmb7ZdtmXQYAALAKBAWrbH5+PseeePKsy4A96qwzTp11CQAAwCox9QAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABitSlBQVV1Vz5l6/dSqOmUn+xxXVcdMvT69qo6/jnVcWlWHX5c+pvq6Yk/0AwAAAPuy1RpRcHWSn9zFi/Tjkhyzs41WoiaMlgAAAIBdtFoX099IclqSpyxeUVW3qKrXV9UHhp/7VNWRSZ6Y5ClVdUFV3W/Y/P5V9Z6q+uj06IKq+u1h361V9Yyh7ciq+khVvTLJxUlus+i4b6yqLVV1SVWdNNV+RVX9UVVdWFXvq6pbDe23q6r3VtVFVfWHU9tvqKp3DXVePFUrAAAArHmredf9hUlOqKqbLGr/iyTP7e57JPmpJC/r7kuTvGRoP7q7zx623ZDkvkkekuSZSVJVP5Lk9knumeToJHevqvsP298+yYu6+87d/fFFx/2F7r57ks1JnlxVNx/ab5Tkfd191yTvSvJLU3W+uLu/P8knp/r5uSRv6e6jk9w1yQWLT7yqTqqquaqa28l7BAAAAPuUdavVcXd/ebi7/+QkV02telCSo6pq4fV3VNWhy3Tzxu6+NskHF+70J/mR4ef84fWhmQQE/5vk4939vmX6enJVPXJYvs2wz+eSfD3Jm4b2LUl+eFi+TyZBRpKckeRZw/IHkry8qg4a6rtgiXM/LZMRFamqXqYeAAAA2OesWlAweF6S85K8Yqrteknu1d1fm95wKjiYdvX0JlO//6S7/2rR/kcmuXKpTqrquEwCint391er6p1JDh5Wz3f3wsX8NfnW9+TbLvK7+13DCIafSHJ6Vf15d79yqeMCAADAWrOqD/zr7s8n+YckT5hqfmuSJy28qKqjh8WvJLnxCrp9S5JfWBiFUFUbq+qWO9nnJkm+MIQEd0xyrxUc55wkjxqWT5iq97ZJPt3dL03ysiR3W0FfAAAAsCbsjW8GeE6S6W8/eHKSzcODCD+YyUMMk+Sfkzxy0cMMv013vzXJ3yV5b1VdlOR12XnA8OYk66rqQ5k862C56QnTfj3Jrw7H2DjVflySC6vq/CQ/m8mzDAAAAGC/UN8cdc9qqKo+9sSTZ10G7FFnnXFq/LsDAADWrqra0t2bl1q3N0YUAAAAAGuEoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgFF196xr2K+tX7++5+fnZ10G7FEbjtiY7Zdtm3UZAADAbqqqLd29eal16/Z2MQeaTZs2ZW5ubtZlAAAAwIqYegAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMDI1yOusq1bt6aqZl0G7LYNR2zM9su2zboMAABgLxEUrLL5+fkce+LJsy4DdttZZ5w66xIAAIC9yNQDAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJChYpKqOrKqLF7WdUlVPrarTq+r4oe1mVXV+Vf38bCoFAACAPU9QsBuq6iZJ3pLktO5+xazrAQAAgD1FULDrDk3yb0n+rrtfPOtiAAAAYE8SFOy6P0/y7u5+7nIbVNVJVTVXVXN7sS4AAAC4zgQF36530v6OJA+vqlsu20H3ad29ubs37/HqAAAAYBUJCr7d55LcdFHbzZJcPiy/OslLkvxrVd14bxYGAAAAq01QsEh3X5Hkk1X1wGTy7QZJHpzk3VPbPDfJ25O8oarWz6RQAAAAWAWCgqU9NsnJVXVBJlMNntHd/zO9QXc/Lcm2JGdUlfcRAACA/cK6WRewL+ruDyZ5wBLtj1/0+uf3Vk0AAACwN7gTDgAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjKq7Z13Dfm39+vU9Pz8/6zJgt204YmO2X7Zt1mUAAAB7UFVt6e7NS61bt7eLOdBs2rQpc3Nzsy4DAAAAVsTUAwAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEa+HnGVbd26NVU16zJgt2w4YmO2X7Zt1mUAAAB7kaBglc3Pz+fYE0+edRmwW84649RZlwAAAOxlph4AAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAACjAzIoqKojq+riRW2nVNVTq+peVXVuVV1QVR+qqlMWbfe8qrqsqg7I9w4AAID927pZF7AP+pskP9PdF1bV9ZPcYWHFEA48Msknkhyb5D9mUyIAAACsDnfFv90tk3wySbr7mu7+4NS645JckuTFSR6990sDAACA1SUo+HbPTfKRqvrHqvrlqjp4at2jk/x9kn9M8hNVddBSHVTVSVU1V1Vze6FeAAAA2GMO1KCgl2vv7v+XZHOStyb5uSRvTpKqWp/kx5O8sbu/nOTcJD+6TCendffm7t68xysHAACAVXSgPqPgc0luuqjtZkk+liTd/T9JXlxVL03y2aq6eZJjkhyW5KKqSpJDklyV5E17qWYAAABYdQfkiILuviLJJ6vqgUlSVTdL8uAk766qn6ghCUhy+yTXJPliJtMOfrG7j+zuI5PcLskPV9Uhe7t+AAAAWC0HZFAweGySk6vqgiTvSPKMYSTBiZk8o+CCJGckOSHJDTIJEv5lYefuvjLJu5M8dO+WDQAAAKvnQJ16kOHbDB6wRPujltnlZkts+5N7ui4AAACYpQN5RAEAAACwiKAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAIBRdfesa9ivrV+/vufn52ddBuyWDUdszPbLts26DAAAYA+rqi3dvXmpdev2djEHmk2bNmVubm7WZQAAAMCKmHoAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADAyNcjrrKtW7emqmZdBixrwxEbs/2ybbMuAwAA2EcIClbZ/Px8jj3x5FmXAcs664xTZ10CAACwDzH1AAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGF3noKCquqqeM/X6qVV1ynXtd6q/X62qC6Z+Lh6Oeafd7O+KPVTXkVV18Z7oCwAAAPYVe2JEwdVJfrKqDt8DfX2b7n5hdx+98JPkzCSv6u4PrcbxAAAA4EC2J4KCbyQ5LclTFq+oqltU1eur6gPDz32G9ouq6rCa+FxVPXZof2VV/fByB6qq+yf5mST/3/D6+lX17KHvrVX1y0P7oVX19qo6bzjWw5foa8lthpECH6qql1bVJVX11qq64bDu7lV1YVVdmORXr+P7BgAAAPucPfWMghcmOaGqbrKo/S+SPLe775Hkp5K8bGg/J8l9ktw5yUeT3G9ov3eS9yx1gKo6LMnpSR7X3V8emp+Q5EtD//dI8ktVdbskX0vyyO6+W5IHJHlOVdWiLne0ze2TvLC775zki0PtSfKKJE/q7rvu6M2oqpOqaq6q5na0HQAAAOxr1u2JTrr7y1X1yiRPTnLV1KoHJTlq6hr9O6rq0CRnJ7l/ko8neXGSk6pqY5IvdPeVyxzmJUnO6O5zptp+JMmmqjp+eH2TTC7ytyX542EEwrVJNia5VZJPTe1by2yTJB/r7guG5S1JjhyCisO6+11D+xlJfmyZ9+O0TEZZpKp6mfMBAACAfc4eCQoGz0tyXiZ33RdcL8m9uvtr0xtW1bsyGbr/XUl+N8kjkxyfSYDwbarqcUlum+Qxi1dlcof/LYu2f3ySWyS5e3fPV9WlSQ5etO8JO9jm6qntrklyw6XqAgAAgP3NHvt6xO7+fJJ/yGQ6wIK3JnnSwouqOnrY9hNJDk9y++7+aJJ3J3lqkndlkar67iR/nOSE7v7GotVvSfIrVXXQsO33VdWNMhlZ8JkhAHhAJiHDYivZZvr8vpjki1V136HphB1tDwAAAGvRHgsKBs/JJABY8OQkm4cHDX4wyROn1p2b5D+H5bMzGfr/7iX6fFqSQ5K8YdHXJN4vk2cefDDJecNXFf5VJqMkXjUc96Ikj03y4SX6Xck2i/18khdW1QWZjGYAAACA/Up1m0K/mqqqjz3x5FmXAcs664xT498DAABwYKmqLd29eal1e3pEAQAAALCGCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABhVd8+6hv3a+vXre35+ftZlwLI2HLEx2y/bNusyAACAvaiqtnT35qXWrdvbxRxoNm3alLm5uVmXAQAAACti6gEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjX4+4yrZu3ZqqmnUZrMCGIzZm+2XbZl0GAADATAkKVtn8/HyOPfHkWZfBCpx1xqmzLgEAAGDmTD0AAAAARoICAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEaCAgAAAGC0poOCqnpEVXVV3fE67H/UDtY/saoeu/sVAgAAwNqypoOCJI9O8u7h9+54RJIlg4KqWtfdL+nuV+5m3wAAALDmrNmgoKoOTXLfJE9I8qih7biqetPUNi+oqscPy8+sqg9W1daq+rOqOibJw5I8u6ouqKrvqap3VtXzqmouya9X1SlV9dRh/1+qqg9U1YVV9fqqOmQvnzIAAACsunWzLuA6eHiSN3f3f1bV56rq7sttWFU3T/LIJHfs7q6qw7r7i1V1ZpI3dffrhu2SZH13bx5enzLVzRu6+6VD+x9mElA8fzVODAAAAGZlzY4oyGS6wauH5Vdnx9MPvpTka0n+uqp+MslXd7Dta5Zpv0tVnV1VFyU5Icmdl+ugqk6qqrlhZAIAAACsGWtyREFV3SzJA5N8f1V1kusn6ST/lG8NPw5Oku7+RlXdM8kPJTk+ya8N+y/lymXaT0/yiO6+cJjOcNxy9XX3aUlOG2rtFZ0UAAAA7APW6oiC45Oc0d237e4ju/s2ST6WyfkcVVU3qKrDMgkGFp5ncJPu/tckT0ly16GfryS58QqPeeMkn6yqgzIZUQAAAAD7nTU5oiCTaQbPWtT2+kweavgPSS7OJDg4f1h34yT/VFUHJ6kkvzm0vzrJS6vqyZmEDztycpJzk3x2+L3SgAEAAADWjOo2Mn41VVUfe+LJsy6DFTjrjFPjnwcAAOBAUFVbFh7kv9hanXoAAAAArAJBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAo+ruWdewX1u/fn3Pz8/PugxWYMMRG7P9sm2zLgMAAGDVVdWW7t681Lp1e7uYA82mTZsyNzc36zIAAABgRUw9AAAAAEaCAgAAAGAkKAAAAABGggIAAABgJCgAAAAARoICAAAAYCQoAAAAAEbrZl3A/m7r1q2pqlmXsd/bcMTGbL9s26zLAAAAWPMEBatsfn4+x5548qzL2O+ddcapsy4BAABgv2DqAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAo1UPCqrqd6vqkqraWlUXVNUPrmCf/1dVDxqWf6OqDtlDtZxSVU/dQ32dXlXH74m+AAAAYF+xbjU7r6p7J3lIkrt199VVdXiS9Tvbr7t/f+rlbyT52yRfvY61rOq5AgAAwP5gtUcUbEhyeXdfnSTdfXmSjVX1hiSpqodX1VVVtb6qDq6qjw7tp1fV8VX15CRHJPmPqvqPqnrYMCrhgqr6SFV9bNj+7lV1VlVtqaq3VNWGof2dVfW8qppL8uvThVXVL1XVB6rqwqp6/cKoheHYf1lV76mqjy6MGqiJFwzH/fckt1zl9w4AAAD2utUOCt6a5DZV9Z9V9aKqOjbJ+UmOHtbfL8nFSe6R5AeTnDu9c3f/ZZLtSR7Q3Q/o7jO7++juPjrJhUn+rKoOSvL8JMd3992TvDzJH011s767N3f3cxbV9obuvkd33zXJh5I8YWrdhiT3zWQ0xDOHtkcmuUOSo5I8Nskxu/WOAAAAwD5sVYfjd/cVVXX3TAKBByR5TZKnJ/mfqrpTknsm+fMk909y/SRnr6TfqvqdJFd19wur6i5J7pLkbVWVoZ9PTm3+mmW6uUtV/WGSw5IcmuQtU+ve2N3XJvlgVd1qaLt/kr/v7muSbK+qd+ygvpOSnLSScwEAAIB9yarP2x8urN+Z5J1VdVGSxyV5V5IfSzKf5N+TnJ7JBf5v76y/4SGHP53JhXuSVJJLuvvey+xy5TLtpyd5RHdfWFWPT3Lc1Lqrpw+5s5oW6+7Tkpw21Nu7uj8AAADMyqpOPaiqO1TV7aeajk7y8UxGDvxGkvd292eT3DyTYf0XL9HNV5LceOjvtklemOSnu/uqYf1HktxieHBiquqgqrrzCsq7cZJPDlMXTljB9u9K8rNVdf3hGQgPWME+AAAAsKas9oiCQ5M8v6oOS/KNJP+dyZD8K5PcKpOL7yTZmuQ7u3upu++nJXlzVW3PZGTCzZO8cZhmsL27f3x44OBfVtVNMjmn5yW5ZCe1nZzJMxE+O/y+8U62/8ckD0zywST/m+S9O9keAAAA1pxa+tqcPaWq+tgTT551Gfu9s844Nf6WAQAAVqaqtnT35qXWrfa3HgAAAABriKAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAIBRdfesa9ivrV+/vufn52ddxn5vwxEbs/2ybbMuAwAAYE2oqi3dvXmpdev2djEHmk2bNmVubm7WZQAAAMCKmHoAAAAAjAQFAAAAwEhQAAAAAIwEBQAAAMBIUAAAAACMBAUAAADASFAAAAAAjNbNuoD93datW1NVsy5jt204YmO2X7Zt1mUAAACwlwgKVtn8/HyOPfHkWZex284649RZlwAAAMBeZOoBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAACjfT4oqKprquqCqrq4ql5bVYfswr5HV9WPr1Jdp1TVU1ejbwAAAJiVfT4oSHJVdx/d3XdJ8vUkT1zJTlW1LsnRSVYlKAAAAID90bpZF7CLzk6yqapuluTlSb47yVeTnNTdW6vqlCTfM7T/b5L7JLlhVd03yZ8kuVOSK7r7z5Kkqi5O8pDuvrSqTk7ymCSfTfKJJFu6+8+q6peSnJRkfZL/TnJid391r50xAAAA7EVrYURBknGEwI8luSjJM5Kc392bkvzfJK+c2vSoJA/q7kcn+f0krxlGJLxmB33fI8lPJbnrcIzNU6vf0N336O67JvlQkifswdMCAACAfcpaGFFww6q6YFg+O8lfJzk3kwv7dPc7qurmVfUdwzZndvdVu3iM+yT5p+7+WpKvVdU/T627S1X9YZLDkhya5C0766yqTspkFAIAAACsKWshKLiqu4+ebqiqHW1/5Q7WfSPfOori4BUc//Qkj+juC6vq8UmO29kO3X1aktOSpKp6BccAAACAfcKamXqwyNlJTkiSqjouyeXd/eUltvtKkhtPvb40yd2G/e6W5HZD+zlJHlpVB1fVoUkeMrXPjZN8sqoOWjgmAAAA7K/WalBwSpK7V9XWJM9M8rhltvuPJEcNX6/4s0len+RmVXVJkl9L8p9J0t0fSHJmkq1J/i2T5yB8aejj5EymOpyT5MOrcjYAAACwj6huI+OTpKoO7e4rquqQJO/K5JsUztsD/faxJ5583QuckbPOODX+RgAAAPYvVbWluzcvtW4tPKNgbzmtqo7K5LkFf7MnQgIAAABYawQFg+7+uVnXAAAAALO2Vp9RAAAAAKwCQQEAAAAwEhQAAAAAI0EBAAAAMBIUAAAAACNBAQAAADASFAAAAAAjQQEAAAAwEhQAAAAAo+ruWdewX1u/fn3Pz8/PuozdtuGIjdl+2bZZlwEAAMAeVFVbunvzUuvW7e1iDjSbNm3K3NzcrMsAAACAFTH1AAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAAAAGAkKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYVXfPuob9WlV9JclHZl0Hu+XwJJfPugh2m89vbfP5rW0+v7XLZ7e2+fzWNp/f2rYWP7/bdvctllqxbm9XcgD6SHdvnnUR7LqqmvPZrV0+v7XN57e2+fzWLp/d2ubzW9t8fmvb/vb5mXoAAAAAjAQFAAAAwEhQsPpOm3UB7Daf3drm81vbfH5rm89v7fLZrW0+v7XN57e27Vefn4cZAgAAACMjCgAAAICRoGCVVNWDq+ojVfXfVfX0WdfDylXVbarqP6rqg1V1SVX9+qxrYtdU1fWr6vyqetOsa2HXVNVhVfW6qvpwVX2oqu4965pYuap6yvDvzYur6u+r6uBZ18TyqurlVfWZqrp4qu1mVfW2qvqv4fdNZ1kjy1vm83v28O/PrVX1j1V12AxLZAeW+vym1v1WVXVVHT6L2tix5T67qnrS8M/fJVX1p7Oqb08RFKyCqrp+khcm+bEkRyV5dFUdNduq2AXfSPJb3X1Uknsl+VWf35rz60k+NOsi2C1/keTN3X3HJHeNz3HNqKqNSZ6cZHN33yXJ9ZM8arZVsROnJ3nworanJ3l7d98+yduH1+ybTs+3f35vS3KX7t6U5D+T/J+9XRQrdnq+/fNLVd0myY8k+d+9XRArdnoWfXZV9YAkD09y1+6+c5I/m0Fde5SgYHXcM8l/d/dHu/vrSV6dyR8Oa0B3f7K7zxuWv5LJhcrG2VbFSlXVrZP8RJKXzboWdk1V3STJ/ZP8dZJ099e7+4szLYpdtS7JDatqXZJDkmyfcT3sQHe/K8nnFzU/PMnfDMt/k+QRe7MmVm6pz6+739rd3xhevi/Jrfd6YazIMv/8Jclzk/xOEg+S20ct89n9SpJndvfVwzaf2euF7WGCgtWxMcknpl5viwvNNamqjkzyA0nOnXEprNzzMvkP7LUzroNdd7skn03yimHqyMuq6kazLoqV6e7LMrmD8r9JPpnkS9391tlWxW64VXd/clj+VJJbzbIYrpNfSPJvsy6Clauqhye5rLsvnHUt7LLvS3K/qjq3qs6qqnvMuqDrSlAAy6iqQ5O8PslvdPeXZ10PO1dVD0nyme7eMuta2C3rktwtyYu7+weSXBnDnteMYS77wzMJfI5IcqOqesxsq+K66MlXY7mruQZV1e9mMpXyVbOuhZWpqkOS/N8kvz/rWtgt65LcLJNpy7+d5B+qqmZb0nUjKFgdlyW5zdTrWw9trBFVdVAmIcGruvsNs66HFbtPkodV1aWZTPl5YFX97WxLYhdsS7KtuxdG8Lwuk+CAteFBST7W3Z/t7vkkb0hyzIxrYtd9uqo2JMnwe80Pnz3QVNXjkzwkyQnte9DXku/JJGi9cPj/mFsnOa+qvnOmVbFS25K8oSfen8nI1jX9MEpBwer4QJLbV9Xtqmp9Jg9zOnPGNbFCQ/r310k+1N1/Put6WLnu/j/dfevuPjKTf+7e0d3uaK4R3f2pJJ+oqjsMTT+U5IMzLIld879J7lVVhwz/Hv2heBjlWnRmkscNy49L8k8zrIVdVFUPzmT63cO6+6uzroeV6+6LuvuW3X3k8P8x25LcbfhvI/u+NyZ5QJJU1fclWZ/k8lkWdF0JClbB8BCZX0vylkz+J+kfuvuS2VbFLrhPkhMzuRt9wfDz47MuCg4QT0ryqqramuToJH8823JYqWEkyOuSnJfkokz+H+O0mRbFDlXV3yd5b5I7VNW2qnpCkmcm+eGq+q9MRok8c5Y1srxlPr8XJLlxkrcN///ykpkWybKW+fxYA5b57F6e5LuHr0x8dZLHrfURPbXG6wcAAAD2ICMKAAAAgJGgAAAAABgJCgAAAICRoAAAAAAYCQoAAACAkaAAAK6Dqrpm+Bqyi6vqtVV1yC7uf2RV/dwerullVXXULmz/+Kp6wTLtnx3O78NV9ZSpdadUVVfV9061/cbQtnl4/QtVdVFVbR3en4cP7adX1ceGfs+rqnsv0X5BVb1nBzVvqaobLGp7Z1V9pKourKpzquoOK30P9oSqelhVPX0X97limfbpv6t/rqrDhvYjh/f4D6e2Pbyq5hc+w6q6w/BeXFBVH6qq04b246rqS1Ptf7BE+8LPg3bzbQBgPyAoAIDr5qruPrq775Lk60meuIv7H5lkjwUFVXX97v7F7v7gHuryNd19dJL7JPndqrrN1LqLkjxq6vVPJ7lkqOPWSX43yX27e1OSeyXZOrXtbw/9Pj3JXy1uH36OWaqgqrpdksu6++olVp/Q3XdN8jdJnr3Evtff0cnurqpa191ndvcz91CX039Xn0/yq1PrPpbkJ6Zej+/74C+TPHfY/05Jnj+17uzhfd+c5DFVdbfp9qmff99D5wHAGiQoAIA95+wk31tVN6uqNw530t9XVZuSpKqOnbpje35V3TjJM5Pcb2h7ynAX/5+GO8L/tXDXd9j/MVX1/mHbv1q46K2qK6rqOVV1YZJ7D/su3NV/9HBX/+KqetZUXz9fVf9ZVe/PJATYoe7+XJL/TrJhqvmNSRZGCXxPki8luXxYd8skX0lyxbD/Fd39sSW6fleS712ifUcenOTNO9lm7HeJ92dH7+Nzq+qSqnp7Vd1i4dyq6s3DKIazq+qOQ/vpVfWSqjo3yZ/W1MiM4c7/O4a/gbdX1XcN7berqvcOn8kfLlH3Ut6bZOPU668m+dDCZ5zkZ5P8w9T6DUm2Lbzo7osWd9jdVybZkl1/7wE4AAgKAGAPqKp1SX4sk7vsz0hy/nAn/f8meeWw2VOT/OpwR/d+Sa7K5I76wt3c5w7b3TPJTyXZlOSnq2pzVd0pkwvC+wz7X5PkhGH7GyU5t7vv2t3vnqrpiCTPSvLAJEcnuUdVPaKqNgw13ifJfZPsdJrCcKF7cL51VMCXk3yiqu6SyciC10ytuzDJp5N8rKpeUVUPXabrhw7v2YJnT4Upr1pmn5UEBdP9ju9Pks9lx+/jXHffOclZSRZCmtOSPKm7757JZ/iiqePcOskx3f2bi47//CR/M/wNvCqTu/xJ8hdJXtzd35/kkzs5h4URED+U5MxFq16d5FHDCI9rkmyfWvfcJO+oqn8bwqfDluj35pmM8lgYiXC/+tapB9+zs9oA2H+tm3UBALDG3bCqLhiWz07y10nOzeRCP939jqq6eVV9R5Jzkvz5cAH8hu7eVlVL9fm24Q5+quoNmVzMfyPJ3ZN8YNjnhkk+M2x/TZLXL9HPPZK8s7s/O/T1qiT3H9ZNt78myfctc34/W1X3T3LHJL/W3V9btP7VmYQEP5rJBe3PD+d9TVU9eKjhh5I8t6ru3t2nDPs9u6p+L8lnkzxhqr/f7u7XLVNLqmp9klt390eX2eRVVXVVkkuTPGlom35/fijLv4/X5pthx98meUNVHZrkmCSvnfqspp+N8NruvmaJOu6d5CeH5TOS/OmwfJ8MfxtD+7OytIW/q41JPpTkbYvWvznJqZmEMdMBTbr7FVX1lkwClYcn+eWquuuw+n5Vdf5wrs/s7kuq6rhMwqqHLFMLAAcYQQEAXDdXDXemR8tc/Ke7n1lV/5Lkx5OcU1U/ukyfvcTryuQO9f9ZYvuvLXOxuie8prt/bRjm/taqOrO7PzW1/k2ZPAtgrru/PH3u3d1J3p/k/VX1tiSvSHLKsHqHgcAO3C/Ju3ew/oTunlvUNv3+7Oh9XKwzGX35xcWf8ZQrV9DPUv3uzFXdfXRNHo75lkyeUbAwKiHd/fWq2pLktzIZEfKwbzlA9/YkL0/y8qq6OMldhlUCAQB2ytQDANjzzs4wnH24W3v5cBH9Pd19UXc/K8kHMrlL/5UkN160/w/X5DkHN0zyiExGIrw9yfFVdcuh35tV1W13Usf7kxxbk6fiXz/JozMZUn/u0H7zqjook4fh7dBw8X1Gkl9f1P7VJE9L8kfT7VV1RH3zQXnJZOrDx3d2nBV4cJJ/uw777+h9vF6S44fln0vy7u7+cibTJ3562L6m7s7vyHvyzQc9npDJ30Qy+Syn23doeH+fnOS3hukt056T5Gnd/fnpxqp68PC5pqq+M8nNk1y2gpoBIIkRBQCwGk7J5E7u1kwePPe4of03quoBmQz7viSTC95rk1wzPGjv9CRfyOQC//WZzH//24U75MNQ/bdW1fWSzGdyl3nZi+/u/mRNvq7vPzK5k/4v3f1PQ1+nZPKQvC8muWCF5/WsJOdV1R8vOs6rl9j2oCR/Njwn4WuZTDFYyTdCLExJWHDP7v761Ovjkvz+Cuv9Nt39wR28j1cmueew/jOZPMsgmVzQv3hoPyiT6RYX7uRQT0ryiqr67UzO/eeH9l9P8ndV9bQk/7TCms8f/pYenW8GDunuS/Kt33aw4EeS/EVVLUwT+e3u/tTCQxiXcb+pKTRJ8oe7OeIDgP1ATUYFAgD7gqp6fJLN3f1rs65lX1OTr1x8aXf/2Cr1f0V3H7oafQPAWmJEAQCwJnT3tky+WQIAWEVGFAAAAAAjDzMEAAAARoICAAAAYCQoAAAAAEaCAgAAAGAkKAAAAABGggIAAABg9P8DJpmsKrdABn4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compute in-space placebos\n",
    "dsc.in_space_placebo(15)\n",
    "\n",
    "#Visualize\n",
    "dsc.plot(['rmspe ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RMSPE ratio of West Germany is lower using the DSC as compared to SC (16 vs 23). Simultaneously, the distribution of placebo units is grouped comparably closely towards 0 using DSC. The highest placebo RMSPE for DSC is 6 (Greece), whereas the highest for SC is 10 (Norway). On the whole, this means that the synthetic control estimate of West Germany is similar in terms of in-space placebo significance using DSC, as compared to SC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable descriptions and data sources:\n",
    "* **GDP per Capita (PPP, 2002 USD):** Source: OECD National Accounts (retrieved via the OECD Health Database). Data for West Germany was obtained from Statistisches Bundesamt 2005 (Arbeitskreis “Volkswirtschaftliche Gesamtrechnungen der Lander”) and converted using PPP ¨monetary conversion factors (retrieved from the OECD Health Database).  \n",
    "* **Investment Rate:** Ratio of real domestic investment (private plus public) to real GDP. The data are reported in five-year averages. Source: Barro, Robert Joseph, and Jong-wha Lee. 1994. “Data Set for a Panel of 138 Countries.” Available at http://www.nber.org/pub/barro.lee/.  \n",
    "* **Schooling**: Percentage of secondary school attained in the total population aged 25 and older. The data are reported in five-year increments. Source: Barro, Robert Joseph, and Jong-wha Lee. 2000. “International Data on Educational Attainment: Updates and Implications.” CID Working Paper No. 42, April 2000 – Human Capital Updated Files.  \n",
    "* **Industry**: industry share of value added. Source: World Bank WDI Database 2005 and Statistisches Bundesamt 2005.  \n",
    "* **Inflation**: annual percentage change in consumer prices (base year 1995). Source: World Development Indicators Database 2005 and Statistisches Bundesamt 2005.  \n",
    "* **Trade Openness**: Export plus imports as percentage of GDP. Source: World Bank: World Development Indicators CD-ROM 2000."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}